1 Introduction

Team 010100 are the following members: Obumneke Amadi, Izzy Illari, Lucia Illari, Omar Qusous, and Lydia Teinfalt. You may find our work over on GitHub.

With the 2020 Olympics beginning this July in Tokyo we felt that a relevant discussion to have would be What makes an Olympian? What can we say about Olympians? Have there been any general trends amongst Olympians? What does the Olympic population look like? These questions are all suited to EDA, and with these questions in mind we went to see if we could find data on Olympians that would be readily available for us to analyze. Eventually our question morphed into the following: are there any specific characteristics (i.e. age, weight, height, BMI, country of origin) that could be used to describe an Olympian in general?

We were able to find a dataset called 120 years of Olympic history: athletes and results on Kaggle over here: https://www.kaggle.com/heesoo37/120-years-of-olympic-history-athletes-and-results. This historical dataset includes all Olympic Games from Athens 1896 to Rio 2016, which was scraped from https://www.sports-reference.com/. This data was compiled by a group of Olympic historians and statisticians. All of these individuals are members of the International Society of Olympic Historians (ISOH) and have been working on this project since the late 1990s.

The report is organized as follows:

1. Summary of Dataset
2. Description of Data/Descriptive Stats
3. BMI of Olympic Athletes
4. Geographical Data
5. Name Data
6. Changes in Weight/Height over the Decades
7. Age Data

2 Summary of Dataset

The data looks like the following:

'data.frame':   271116 obs. of  15 variables:
 $ ID    : int  1 2 3 4 5 5 5 5 5 5 ...
 $ Name  : Factor w/ 134732 levels "  Gabrielle Marie \"Gabby\" Adcock (White-)",..: 8 9 44318 29412 21469 21469 21469 21469 21469 21469 ...
 $ Sex   : Factor w/ 2 levels "F","M": 2 2 2 2 1 1 1 1 1 1 ...
 $ Age   : int  24 23 24 34 21 21 25 25 27 27 ...
 $ Height: int  180 170 NA NA 185 185 185 185 185 185 ...
 $ Weight: num  80 60 NA NA 82 82 82 82 82 82 ...
 $ Team  : Factor w/ 1184 levels "30. Februar",..: 199 199 273 278 705 705 705 705 705 705 ...
 $ NOC   : Factor w/ 230 levels "AFG","AHO","ALB",..: 42 42 56 56 146 146 146 146 146 146 ...
 $ Games : Factor w/ 51 levels "1896 Summer",..: 38 49 7 2 37 37 39 39 40 40 ...
 $ Year  : int  1992 2012 1920 1900 1988 1988 1992 1992 1994 1994 ...
 $ Season: Factor w/ 2 levels "Summer","Winter": 1 1 1 1 2 2 2 2 2 2 ...
 $ City  : Factor w/ 42 levels "Albertville",..: 6 18 3 27 9 9 1 1 17 17 ...
 $ Sport : Factor w/ 66 levels "Aeronautics",..: 9 33 25 62 54 54 54 54 54 54 ...
 $ Event : Factor w/ 765 levels "Aeronautics Mixed Aeronautics",..: 160 398 349 710 623 619 623 619 623 619 ...
 $ Medal : Factor w/ 3 levels "Bronze","Gold",..: NA NA NA 2 NA NA NA NA NA NA ...

The athlete events data has 15 columns and 271116 rows/entries, for a total of 4066740 individual data points. In athelete_events each row corresponds to an individual athlete competing in an individual Olympic event. The variables are the following:

1. ID: Unique number for each athlete
2. Name: Athlete’s name
3. Sex: M or F
4. Age: Integer
5. Height: centimeters
6. Weight: kilograms
7. Team: Team name
8. NOC: National Olympic Committee 3-letter code
9. Games: Year and season
10. Year: Integer
11. Season: Summer or Winter
12. City: Host city
13. Sport
14. Event
15. Medal: Gold, Silver, Bronze, or NA

To prepare our data for EDA we dropped the Olympic event: Art Sculpting. NAs were also removed.

3 Description of Data/Descriptive Stats

We can look at the top 10 events by number of athetes participating in these events. We can show this in a table or in a bar chart.

        sport.names sport.counts
Var1.55    Swimming         2486
Var1.44      Rowing         2104
Var1.31  Ice Hockey         1301
Var1.30      Hockey         1168
Var1.28  Gymnastics         1161
Var1.23     Fencing         1109
Var1.25    Football         1084
Var1.15    Canoeing         1041
Var1.9   Basketball         1000
Var1.66   Wrestling          967

In list form:

1. Swimming
2. Rowing
3. Ice Hockey
4. Hockey
5. Gymnastics
6. Fencing
7. Football
8. Canoeing
9. Basketball
10. Wrestling

4 BMI of Olympic Athletes

5 Graphical Representation of Data

6 Geographical Data

7 Olympic Name Data

8 Trends Over Time

We wanted to see the changes in Olympians for the top 10 events (in terms of how many Olympians were recorded for the sport) over the years. Below are the top 10 events:

It seems that in general events like rowing or basketball have been trending towards larger and larger athletes (in terms of both weight and height) whereas events like Gymnastics skews in the completely opposite direction, favoring smaller and smaller athletes. The Wrestling event data is a bit misleading—we have taken the averages per Olympic game year, and if we were to look at the Wrestling data by itself we would see clear categories for weight and height. This is due to the nature of the sport, and its weight classes. This is why in the men’s data it looks like the data for the Wrestling is a constant straight line. It appears that way because there the following weight classes at the Olympics: Freestyle weight classes (12): Men’s 57kg, Men’s 65kg, Men’s 74kg, Men’s 86kg, Men’s 97kg, Men’s 125kg, Women’s 50kg, Women’s 53kg, Women’s 57kg, Women’s 62kg, Women’s 68kg, Women’s 76kg; and then Greco-Roman weight classes (6): Men’s 60kg, Men’s 67kg, Men’s 77kg, Men’s 87kg, Men’s 97kg, Men’s 130kg.

What we can also use this over time data for is to see trends during “decades”. We can overlay the data for the decades ontop of each other on a histogram, or we could use boxplots where we’ve made a decade into a “rank”/factorial data type.

Now that I’ve created a new comlumn Decade in the dataframe, I can make histograms or boxplots of the Weight and Height and group by Decade.

9 Age Data

It appears that the mean age that an Olympic Medalist wins a medal is around 26 years old. We can also look at the ages of medal-winning athletes separated by the Summer and Winter Games.

From the plots we can see that between WW1 and WW2 the average age of medalists is decreasing, but after WW2 the average age temporarily rose. We see that the age begins to decrease until 1980 but then rises again after 1980. The age seems to plateau in the 2010s.

Now we can look at the trends amongst the athletes in the Winter Games.

It seems that there are fewer peaks and dips than in the Summer Games data, where the Winter Athletes seem to have a smaller variance in age. We can look at the Summer and Winter Games together.

It appears that after the 1950s the athletes at the Winter Games, on average, are older. Both Summer and Winter Games experience an upward trend in ages after the 1980s.

We can also look at the breakdown of ages between Season and Gender.

When we look at the medal-winning athletes during the Summer Games by gender we see that in general men get medals at older ages than women do.

We can also separate the Winter Game data by gender, as we did for the Summer Games.

Just as with the Summer Games we see that, on average, male athletes tend to be older than female athletes.

10 Discussion & Conclusion